The application discloses a kind of multivariate
time series unsupervised dimension reduction method based on global-local
divergence, it includes the following steps: S1, the
covariance matrix of multivariate
time series is calculated, the upper
triangular element of
covariance matrix is extracted, it is combined as feature sequence, obtain the
feature set Fea of multivariate
time series={f i |i=1,2,…,n};S2, using k
near neighbor and Euclid Distance (Euclid Distance, ED) measure is established neighborhood set N k (f i )={f j |j=1,2,…,k};S3, after finding the neighborhood of each sample point, the neighborhood center sequence m i Of each feature sequence f i In
feature set is calculated;S4, with the neighborhood variance of sample point after projection, local
divergence is characterized.First, the variance of each sample point neighborhood set after projection is calculated, then the sum of the accumulation of these variances is obtained, to obtain local
divergence;S5, according to the field center point obtained in step S3, the variance of field center point is calculated to obtain global variance.Finally, the low-dimensional projection sequence obtained by the experimental result shows that the method can represent original MTS, and obvious dimension reduction is realized.